Çankaya GCRIS Standart veritabanının içerik oluşturulması ve kurulumu Research Ecosystems (https://www.researchecosystems.com) tarafından devam etmektedir. Bu süreçte gördüğünüz verilerde eksikler olabilir.

Bilgisayar Programcılığı Bölümü

Permanent URI for this communityhttps://hdl.handle.net/20.500.12416/7433

Browse

Browsing Bilgisayar Programcılığı Bölümü by Scopus Q "N/A"

Filter results by typing the first few letters
Now showing 1 - 3 of 3
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 0
    A Graphical Speech Analysis Teaching Tool
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.; Özaydın, Selma; 253019; Çankaya Meslek Yüksekokulu
    The widespread use of digital speech processing in today's technologies causes many electronics and computer engineering students to need a basic background in these subjects. The paper describes a toolbox designed to support undergraduate or graduate level courses on speech processing. The proposed educational toolbox is designed as a virtual lab for basic operations in digital speech processing-based courses. This graphical user interface (GUI) based speech analysis algorithm is built with six main function modules, which are signal input, noise addition, up-sampling/down-sampling, time domain feature analysis, pitch detection and frequency domain analysis. The toolbox involves different operations for measuring important speech feature parameters such as pitch, energy, zero-crossing ratio, FFT and power spectrum of an input speech signal. The toolbox has also been developed to easily manipulate and add some other possible speech processing methods. It is thought that the tool will make it easier for students to understand the methods that form the basis of digital speech processing, increase the interest in the lesson with its visual outputs, and allow new methods to be added easily when desired thanks to its simple and modular structure. The main aim of this paper to show how such a tool facilitates students understanding of technical concepts introduced in speech courses. © 2022 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 1
    Design of a Voice Activity Detection Algorithm based on Logarithmic Signal Energy
    (Institute of Electrical and Electronics Engineers Inc., 2022) Ozaydin, S.; Özaydın, Selma; 253019; Çankaya Meslek Yüksekokulu
    This article presents a new method for calculating the signal energies of speech segments in voice activity detection algorithms. In the study, the μ-law signal compression method is adapted to calculate short-term signal energies. A simple voice activity detection (VAD) algorithm is designed to demonstrate the effectiveness of the proposed method. The same VAD algorithm was also run with two different conventional energy calculation formulas and the performance of each VAD was evaluated using time-domain short-time energy features. The G729 standard VAD algorithm was also used for performance comparison. During the test of the analyzed detectors, many kinds of input speech signals with various types of background environmental noise, such as restaurants, vehicles, and streets, were tested. Using the new energy calculation method, the VAD detector has improved detection accuracy compared to VAD detectors based on the other two energy methods and was able to effectively identify voice-active regions even in noisy conditions at low SNR levels. The results revealed that the VAD detector designed with the proposed new energy calculation formula outperforms traditional energy-based voice activity detection methods and provides noticeable increases in detection rate even under adverse conditions. © 2022 IEEE.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 5
    Speech Denoising with Maximal Overlap Discrete Wavelet Transform
    (Institute of Electrical and Electronics Engineers Inc., 2022) Alak, I.K.; Özaydın, Selma; Ozaydin, S.; 253019; Çankaya Meslek Yüksekokulu
    In this paper, the effectiveness of the maximum overlapping discrete wavelet transform (MODWT) method on denoising the speech signal is tested and examined. Ensuring the intelligibility of the speech signal in noisy environments by separating it from the noise is a widely researched topic today. On the other hand, being able to recover the original speech from the noisy signal with minimal distortion is a challenge due to the difficulties in removing the background noise. Numerous factors in environmental noise environments can interfere with the signal. In this study, the performance of some discrete wavelets transform methods is experimentally analyzed using different wavelet filters. The analysis program was carried out in the MATLAB environment. As the input noise speech signal, speech sounds containing different environmental background noises (train, car, station, plane, etc.) were analyzed. During the tests, these noisy input signals were filtered out from the speech signal by wavelet analysis. The input noisy speech signal is decomposed into wavelet coefficients with different thresholding methods. The reconstructed speech was compared by measuring the signal-to-noise ratio (SNR) values between the noisy input signal and the smoothed output signals. The scientific contributions of the study include a detailed comparative analysis of the performances of various wavelet methods against different background environmental noises. © 2022 IEEE.